doc/v628/rf408__RDataFrameToRooFit_8py.html

import ROOT

import math


# Set up

# ------------------------


# We create an RDataFrame with two columns filled with 2 million random numbers.

d = ROOT.RDataFrame(2000000)

dd = d.Define("x", "gRandom->Uniform(-5.,  5.)").Define("y", "gRandom->Gaus(1., 3.)")


# We create RooFit variables that will represent the dataset.

x = ROOT.RooRealVar("x", "x", -5.0, 5.0)

y = ROOT.RooRealVar("y", "y", -50.0, 50.0)

x.setBins(10)

y.setBins(20)


# Booking the creation of RooDataSet / RooDataHist in RDataFrame

# ----------------------------------------------------------------


# Method 1:

# ---------

# We directly book the RooDataSetHelper action.

# We need to pass

# - the RDataFrame column types as template parameters

# - the constructor arguments for RooDataSet (they follow the same syntax as the usual RooDataSet constructors)

# - the column names that RDataFrame should fill into the dataset

#

# NOTE: RDataFrame columns are matched to RooFit variables by position, *not by name*!

rooDataSet = dd.Book(

    ROOT.std.move(ROOT.RooDataSetHelper("dataset", "Title of dataset", ROOT.RooArgSet(x, y))), ("x", "y")

)


# Method 2:

# ---------

# We first declare the RooDataHistHelper

rdhMaker = ROOT.RooDataHistHelper("dataset", "Title of dataset", ROOT.RooArgSet(x, y))


# Then, we move it into an RDataFrame action:

rooDataHist = dd.Book(ROOT.std.move(rdhMaker), ("x", "y"))


# Run it and inspect the results

# -------------------------------


# Let's inspect the dataset / datahist.

# Note that the first time we touch one of those objects, the RDataFrame event loop will run.


def printData(data):

    print("")

    data.Print()

    for i in range(min(data.numEntries(), 20)):

        print(

            "("

            + ", ".join(["{0:8.3f}".format(var.getVal()) for var in data.get(i)])

            + ", )  weight={0:10.3f}".format(data.weight())

        )


    print("mean(x) = {0:.3f}".format(data.mean(x)) + "\tsigma(x) = {0:.3f}".format(math.sqrt(data.moment(x, 2.0))))

    print("mean(y) = {0:.3f}".format(data.mean(y)) + "\tsigma(y) = {0:.3f}\n".format(math.sqrt(data.moment(y, 2.0))))


printData(rooDataSet)

printData(rooDataHist)

format
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t format
Definition TGWin32VirtualXProxy.cxx:249

ROOT::RDataFrame
ROOT's RDataFrame offers a modern, high-level interface for analysis of data stored in TTree ,...
Definition RDataFrame.hxx:41